Package: ActiveLearning4SPM
Type: Package
Title: Active Learning for Process Monitoring
Version: 0.1.0
Authors@R: c(
    person("Christian", "Capezza", role = c("aut", "cre"),
           email = "christian.capezza@unina.it"),
    person("Antonio", "Lepore", role = "aut"),
    person("Kamran", "Paynabar", role = "aut"))
Description: Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) 
    <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. 
    The package provides functions to (i) simulate data streams with true latent states and 
    multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) 
    using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based 
    active learning that balances exploration and exploitation to decide whether to request labels 
    in real time. The methodology is particularly suited for statistical process monitoring 
    in industrial applications where labeling is costly.
Depends: R (>= 4.2)
Imports: Rcpp, Rfast, mvnfast, rrcov, caTools, abind, pROC, stats
LinkingTo: Rcpp, RcppArmadillo
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: covr, testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2025-09-30 12:31:47 UTC; christian.capezza
Author: Christian Capezza [aut, cre],
  Antonio Lepore [aut],
  Kamran Paynabar [aut]
Maintainer: Christian Capezza <christian.capezza@unina.it>
Repository: CRAN
Date/Publication: 2025-10-07 18:30:21 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 21:28:57 UTC; unix
Archs: ActiveLearning4SPM.so.dSYM
